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Concepts of Database Management
Concepts of Database Management
General data management concepts
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ETL and Data Management ETL Detailed Discussion The system of ETL is in general utilized to join in the data from numerous applications in the systems, characteristically established as well as reinforced by a number of existing vendors or others held on distinct hardware of the computer. The distinct systems comprising the actual data is most repeatedly accomplished as well as run by a number of employees. Referring to example of system used for cost accounting, it is evident that this system would thereby collate the information flow from payroll, transactions as well as acquiring. In the process of ETL, the initial phase comprise of the data extraction from the number of sources in the existing systems. In numerous circumstances this refers to the actual challenging factor of the process of ETL, subsequently the data extraction appropriately initiate the efficacy platform for by what means succeeding developments would further advance. The second phase of transformation in ETL process implies a chain of guidelines along with the necessary functions applied on the data after extraction from its source to develop the output data for effectively loading (Wyatt, L., Caufield, B., & Pol, 2009). A number of sources of data need precisely slight or sometimes absolutely no data manipulation. The last phase of data loading on the target end typically referred as the data warehouse. On the basis of the necessities of the businesses, the ETL overall process differs extensively. A number of data warehouse possibly will overwrite the present data by means of collective information; commonly, appraising the data which is extracted carried out based on the frequency of day-to-day, week on week, or month on month. ETL and ELT Con... ... middle of paper ... ...adis, P., Karagiannis, A., Tziovara, V., Simitsis, A., & Hellas, I. (2007). Towards a benchmark for ETL workflows. Vassiliadis, P., Simitsis, A., & Skiadopoulos, S. (2002, January). On the logical modeling of ETL processes. In Advanced Information Systems Engineering (pp. 782-786). Springer Berlin Heidelberg. Hellerstein, J. M., Stonebraker, M., & Caccia, R. (1999). Independent, open enterprise data integration. IEEE Data Eng. Bull., 22(1), 43-49. Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4), 3-13. Wyatt, L., Caufield, B., & Pol, D. (2009). Principles for an ETL Benchmark. InPerformance Evaluation and Benchmarking (pp. 183-198). Springer Berlin Heidelberg. Wang, Y. Z., & Li, H. B. (2002). Design and Implementation of Data ETL Tools Basing on OLE DB. MINIMICRO SYSTEMS-SHENYANG-, 23(4), 453-455.
ETL is a three-step process which stands for Extract-Transform-Load. This process comprises of: extracting the desired data from a source, transforming the extracted data into a specific format, and loading the transformed data into a destination such as a data warehouse (Haag & Cummings, 2013). After the ETL process is performed, data-mining tools can be used to turn this data into useful information. For the first three questions, the database would need to capture each checkout price, how many items are purchased, the individual price of each item, and if the item is discounted or full MSRP. This specific data will likely originate from a customer oriented database that will then flow into the data warehouse for full ETL. For YTD profits, the database would need to capture all purchases, sales, profits, and expenses from the current year. Sport T’s company data will originate from an in-company database which focuses on business expenses and profits. In solving customer satisfaction, the KPIs to consider would be survey questions and answers from responding customers as well as customer opinion on what can be improved. For customer surveys, we will ask
In today's competitive marketplace, all firms are seeking ways to improve their overall performance. One such method of improvement, recently adopted by many firms, is benchmarking. Benchmarking is a technique used to evaluate internal business processes. "In this analysis, managers determine the firm's critical processes and outputs, baseline those processes, then compare the performance of each process against a standard outside the industry" (Bounds, Yorks, Adams, & Ranney 1994). To effectively improve a business process to world-class quality, managers must find a firm that is recognized as a global leader, not just the industry standard. Successful benchmarking requires tailor-made solutions, not just blind copying of another organization. Measurement and interpretation of data collected is the key to creating business process solutions.
Smith, W., & Jewett, D. (2009). Tableau software and teradata database the visual approach to the active data warehouse. In Retrieved from http://www.tableausoftware.com/learn/whitepapers
File transfers requires to be scheduled for data to be moved to a central database or data warehouse. This by far involves an Extract, Transform and Load (ETL) workflow as data is usually gathered from different types of database. Once the data is brought to a central database, to determine patterns in the data queries are scheduled from a variety of users using various applications. The frequency of the queries varies from business to business – it can be continuous, once a day or hourly. And of course, as data gets added to the database and moved to new databases, there is the routine task of database management that needs to occur.
... different layers such as ETL stage, SIF, BDW and how data is processed to generate reports according to the requirement. The processing of information from raw data to different processing stages culminating in coherent information is fascinating.
This white paper identifies some of the considerations and techniques which can significantly improve the performance of the systems handling large amounts of data.
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Wang, R. Y. Pierce, E. M. and Madnick, S. E. (2005) Information Quality. Armonk: M.E. Sharpe, Inc..
- Scheer, A, Habermann, F, 2000, "Making ERP a success", Communications of the ACM, 43 , 3, 57-61.
[1]- Ralph Stair, George Reynolds and Thomas Chesney. 2012. Fundamentals of Business information systems. 2nd edition: Cengage Learning EMEA.
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources necessary to properly analyze the data, and as a result, companies often made business d...
As data remains one of the most important aspects of every business, companies are gradually placing lots of importance on the quality of data used. Databases use different formats or styles. This can make the data collected to be extremely clumsy and sometimes unintelligible.
Data Collection is the process of collecting information that will be utilized in the diagnostic process and eventually used to make business recommendation. In this data collection process, it is critical to ensure the highest quality of data possible. In the data collection component, the information is gathered on the specific department or organization such as inputs, design components, an...
Individuals and organizations within current information society have at their disposal vast amount of data which potential has not fully been used. With the increasing availability of data there is a need to organize data and to extract knowledge from such data. Data are being accumulated in different formats and databases, which are usually not connected together, therefore leading to the inefficient use of valuable information.